Vector-based shock indication

ABSTRACT

A system for managing care of a person receiving emergency cardiac assistance includes one or more capacitors arranged to deliver a defibrillating shock to a person; one or more electronic ports for receiving a plurality of signals from sensors for obtaining indications of an electrocardiogram (ECG) for the person; and a patient treatment module executable on one or more computer processors using code stored in non-transitory media and to provide a determination of a likelihood of success from delivering a future defibrillating shock to the person with the one or more capacitors, using a mathematical computation applied to a vector value defined by signals from at least two of the plurality of signals.

CLAIM OF PRIORITY

This application claims priority under 35 USC §119(e) to U.S. PatentApplication Ser. No. 61/953,195, filed on Mar. 14, 2014, the entirecontents of which are hereby incorporated by reference.

TECHNICAL FIELD

This document relates to cardiac resuscitation systems and techniques.

BACKGROUND

Heart attacks are a common cause of death. A heart attack occurs when aportion of the heart tissue loses circulation and, as a result, becomesdamaged (e.g., because of blockage in the heart vasculature). Heartattacks and other abnormalities can lead to ventricular fibrillation(VF), which is an abnormal heart rhythm (arrhythmia) that causes theheart to lose pumping capacity. If such a problem is not correctedquickly—typically within minutes—the rest of the body loses oxygen andthe person dies. Therefore, prompt care of a person undergoingventricular fibrillation can be key to a positive outcome for such aperson.

One common way to treat ventricular fibrillation is through the use ofan electrical defibrillator that delivers a relatively high voltageshock to the heart in order to force it back to a normal, consistent,and strong rhythm. People who have had previous problems withventricular fibrillation may be implanted with an automaticdefibrillator that constantly monitors the condition of their heart andapplies a shock when necessary. Other such people may be provided with awearable defibrillator in the form of a vest such as the LIFEVESTproduct from ZOLL Lifecor Corporation of Pittsburgh, Pa. Other peoplemay be treated using an external defibrillator, such as in a hospital orvia an automatic external defibrillator (AED) of the kind that isfrequently seen in airports, public gymnasiums, and other public spaces.Defibrillation may be delivered in coordination with cardiopulmonaryresuscitation, which centers around the provision of repeatedcompressions to a victim's chest, such as by a rescuer pressing downwardrepeatedly with the palms of the hands, or via a mechanical compressiondevice such as the AUTOPULSE non-invasive cardiac support pump from ZOLLMedical Corporation of Chelmsford, Mass.

People undergoing ventricular fibrillation may be more receptive to adefibrillating shock in some instances compared to others. For example,research has determined that a computation of amplitude spectrum area(AMSA), or other computational methods that use either time-based orspectrum-based analytic methods on electrocardiogram (ECG) data tocalculate a prediction of defibrillation shock success, may indicatewhether a shock that is delivered to a person will likely result insuccessful defibrillation or will instead likely fail.

SUMMARY

This document describes systems and techniques that may be used to helpdetermine when a shock on a person suffering from VF will likely besuccessful, i.e., will successfully defibrillate the person. By makingsuch a determination, a medical device like a defibrillator can enabledelivery of a shock only when the likelihood exceeds some thresholdvalue (where the value is determined by professionals to have thebenefit of likely defibrillation outweigh the dis-benefits of harmingthe patient). Alternatively, or in addition, the device can indicate toa rescuer one of a plurality of likelihood values so that the rescuercan make an informed decision about whether to deliver a shock.

The techniques discussed here receive input from a plurality of ECGleads (e.g., from a 12-lead system) and characterize that input as avector value, where the vector that may be made up of three orthogonal(X, Y, and Z) vectors from the plurality of leads and can be understoodas rotating through a complex space with each cycle of a heartbeat. Acomplex FFT operation may then be conducted on the vector representationin order to compute a vectorized amplitude spectrum area (AMSA) value,where the AMSA value is a numerical value that is based on the sum ofthe magnitude of a weighted frequency distribution from the signal,e.g., between 3 and 48 Hz. Generally, the greater the AMSA, the greaterthe probability that an applied shock will defibrillate the heartsuccessfully.

Particular techniques discussed here, including selection of properwindow size for the ECG data, proper window type, proper coefficients,and the use of vectorized operations in calculating the AMSA, mayimprove the quality of the AMSA scoring process. An AMSA score may alsobe used to determine where, time-wise, a person is in the process ofsuffering from cardiac arrest and fibrillation, since defibrillatingshocks may be much less effective after a person has been fibrillatingfor several minutes, and CPR (including forceful CPR) may be a preferredmode of treatment instead. Such systems may also combine a current AMSAvalue (e.g., for recommending a shock) with a trend in AMSA value overtime (e.g., for recommending chest compressions instead of a shock),where some or all of the AMSA values may be made from vectorized input.

Correlations between particular AMSA values and other inputs fromsources other than an ECG (e.g., trans-thoracic impedance), on the onehand, and the likelihood that a future shock will generate a successfuldefibrillation, on the other hand, may be determined by analysis ofhistorical defibrillation activity (e.g., activity collected andreported by portable defibrillators deployed in the field for hundredsor thousands actual cardiac events), and may be used to produce amapping between observed past likelihood of success for various AMSAvalues and levels of prior successful defibrillations. Such data may beused, for example, to generate a look-up table or similar structure thatcan be loaded on other deployed or to-be-deployed defibrillators, whichcan be consulted in the future during other cardiac events to express afuture likelihood of success that is based on the past observed successor lack of success for a corresponding AMSA value or other predictivevalue.

The particular parameters for computing the vectorized AMSA value may beselected so as to maximize the predictive capabilities of a medicaldevice. For example, a tapering function may be applied to the ECG datawindow (e.g., by using a Tukey window), so as to improve the accuracy ofthe FFT applied to the data. Such a tapered window may prevent the datafrom jumping immediately from a zero value up the measured values, andthen back down immediately to a zero value at the end of a measuredwindow. Various parameters for the tapering function may also beapplied, such as coefficients to define the slopes of the starting andending edges of the function. Moreover, the length of the window may beselected to provide better data, such as by using a relatively shortwindow having a duration shorter than 4 seconds, and in certain examplesof about 1 second, between 1 and 2 seconds, between 1 and 3 seconds,between 2 and 3 seconds, or between 3 and 4 seconds long.

In certain other implementations, multiple different tapering functionsmay be applied to the same data essentially simultaneously, AMSA valuesmay be determined from each such applied function, and the resultingAMSA value from one of the functions may be selected, or an AMSA valuemay be generated that is a composite from multiple different taperingfunctions. The window function that is used, the length of the window,and the coefficients for the window may also be adjusted dynamically, sothat one or more of them change during a particular incident, ordeployment, with a particular patient. For example, it may be determinedfrom analysis of prior data that a certain window shape, size, and/orcoefficients are better earlier in an episode of VF than later, so thata defibrillator may be programmed to change such parameters over thecourse of an event. Such changes may be tied to an initial determinationabout how long the patient has been in VF, which may be a function ofuser input (e.g., when the emergency call was made) and parametersmeasured by the defibrillator. Also, changes to the window type, size,and coefficients may be made from readings dynamically made from thepatient under treatment. For example, AMSA values in a particular rangemay be measured better by a particular window type, size, or range ofcoefficients, so that an AMSA measurement made at time n that shows sucha value, may be measured using the other parameters known to work bestwith that AMSA value at time n+1. Other techniques for dynamicallyadjusting the window type, window size, and/or coefficients may also beemployed.

Upon a defibrillator making a determination of a likelihood of futuresuccess for defibrillating a patient, the defibrillator may provide anindication to a rescuer about such a determination. For example, thedefibrillator may only allow a shock to be performed when the indicationis sufficiently positive (e.g., over a set percentage of likelihood ofsuccess)—and may only provide a “ready for shock” light or otherindication in such a situation. Also, a defibrillator may provide adisplay—such as a graphic that shows whether defibrillation will likelysucceed (e.g., above a predetermined threshold level of likelihood ofsuccess) or provide a number (e.g., a percentage of likelihood ofsuccess) or other indication (e.g., a grade of A, B, C, D, or F) so thatthe rescuer can determine whether to apply a shock. In some situations,the AMSA value may serve merely to provide a recommendation to the user,with the user able to apply a shock at any time; in other situations(e.g., especially for AEDs to be used by lay rescuers), the AMSA valuemay be used to disable or enable the ability to deliver a shock.

The device (e.g., defibrillator) can also change the indication itpresents in different situations, e.g., a dual-mode defibrillator couldsimply indicate whether defibrillation is advised when the defibrillatoris in AED mode (and may refuse to permit delivery of a shock when it isnot advised), and may provide more nuanced information when thedefibrillator is in manual mode, and thus is presumably being operatedby someone who can better interpret such nuanced information and actproperly on it.

With respect to indications of where a victim is in the process of a VFepisode—e.g., how many minutes since the victim's episode has started—anaverage AMSA value (including as a vectorized AMSA value) may bedetermined over a time period so as to identify more generalized changesin the victim's AMSA values, rather than AMSA at a particular point intime or small slice of time. For example, AMSA values can be computedfor particular points in time or particular windows in time and thosevalues can be saved (e.g., in memory of a patient monitor ordefibrillator). After multiple such measurements and computations havebeen made, an average may be computed across multiple such values.Because AMSA generally falls (on average) over time in an episode, ifthe average for a certain number of readings (e.g., a moving average)falls below a particular value or falls below the value over a minimumtime period (so as to indicate the general AMSA condition of the victimrather than just a transient reading), the device may provide additionalfeedback to a rescuer.

These general phases of cardiac arrest or VF may be identified, in onerepresentation, as three separate phases (though there may be someoverlap at the edges of the phases): electrical, circulatory, andmetabolic. The electrical phase is the first several minutes of anevent, and marks a period during which electric shock can beparticularly effective in defibrillating the victim's heart andreturning the victim to a relative satisfactory condition. Thecirculatory phase appears to mark a decrease in effectiveness forelectric shock in defibrillating the victim, and particularly in theabsence of chest compressions performed on the victim. As a result, adevice such as a portable defibrillator may be programmed to stopadvising shocks during such a phase (or may advise a shock only whenother determinations indicate that a shock would be particularly likelyto be effective) and may instead advise forceful CPR chest compressions.Such forceful compressions may maximize blood flow through the hearttissue and other parts of the body so as to extend the time that thevictim may survive without lasting or substantial damage.

In the metabolic phase, chest compressions may be relatively ineffectiveas compared to the circulatory phase. For example, where tissue hasbecome ischemic, such as in circulatory phase, the tissue may reactfavorably to the circulation of blood containing some oxygen, but wheretissue has become severely ischemic, such as in the metabolic phase, theintroduction of too much oxygen may be harmful to the tissue. As aresult, more gentle compressions for the first period, such as 30seconds, may need to be advised in the metabolic phase before therescuer (or a mechanical chest compressor controlled to provideappropriate levels of compression following the points addressed here)uses a full force.

Other treatments that may be useful in the metabolic phase includeextracorporeal circulation and cooling, either alone, in combinationwith each other, or in combination with other pharmacologicaltreatments. In any event, observation of elapsed time since an event hasbegun and/or observation of the phase in which a victim is in, may beused to control a device or instruct a rescuer to switch from a firstmode of providing care to a second mode of providing care in which theparameters of the provided care differ (e.g., speed or depth of chestcompressions may change, temperature-based therapy may be provided orstopped, or pharmaceuticals may be administered).

In certain implementations, such systems and techniques may provide oneor more advantages. For example, determinations of whether a shockshould be provided or what advice to provide a rescuer based on AMSAvalues can be made from variables that are measured for a patient forother purposes (e.g., trans-thoracic impedance and ECG readings). TheAMSA values can be improved with respective to their predictivequalities by actions such as monitoring ECG vectors and performingvectorized FFT operations to produce an AMSA value. In particular, adefibrillator may cause a rescuer to wait to provide a defibrillatingshock until a time at which the shock is more likely to be effective. Asa result, the patient may avoid receiving an ineffective shock, and thenhaving to wait another cycle for another shock (which may end up beingequally ineffective), and avoid the physical harm caused by anydelivered shocks. And a system may guide the rescuer in providing ashock, versus providing deep chest compressions, versus providingprogressive chest compressions (or may cause a device to provide suchactions automatically), throughout the course of a cardiac event. Such aprocess may, therefore, result in the patient returning to normalcardiac function more quickly and with less stress on his or her cardiacsystem, which will generally lead to better patient outcomes.

In one implementation, a system for managing care of a person isdisclosed and comprises one or more capacitors arranged to deliver adefibrillating shock to a patient; one or more electronic ports forreceiving a plurality of signals from sensors for obtaining indicationsof an electrocardiogram (ECG) for the patient; and a patient treatmentmodule executable on one or more computer processors using code storedin non-transitory media and arranged to provide a determination of alikelihood of success from delivering a future defibrillating shock tothe person with the one or more capacitors, using a mathematicalcomputation applied to a vector value defined by signals from at leasttwo of the plurality of signals. The mathematical computation maycomprise one or more vectorized Fast Fourier Transforms (FFTs), and/orone or more amplitude spectrum area calculations. The patient treatmentmodule can be arranged to apply a pre-transform to the plurality ofsignals before applying the mathematical computation so as to make theplurality of signals orthogonal or near orthogonal to each other. Thepre-transform can be applied in response to determining that theplurality of signals were not previously orthogonal or near orthogonal,and the patient treatment module can also be programmed to apply themathematical computation to the vector value by calculating FFT for eachof the plurality of signals to create processed values and then combinethe processed values.

In some aspects, combining the processed values comprises determining aroot of a sum of the processed values. Also, the mathematicalcomputation can comprise a mathematical transform from a time domain toa frequency domain on a window of data. The window can comprise atapered window, which may in turn comprise a Tukey window, and can bebetween about one second and about 2 seconds in width. The window canalso be selected from a group consisting of Tukey, Hann,Blackman-Harris, and Flat Top. The system can further include an outputmechanism arranged to present, to a user of the system, an indicationregarding the likelihood of success from delivering a defibrillatingshock with the one or more capacitors to the person. The outputmechanism may comprise a visual display, and the system is programmed todisplay to the user one of multiple possible indications that eachindicate a degree of likelihood of success, may also comprise aninterlock that prevents a user from delivering a shock unless thedetermined likelihood of success exceeds a determined value, and mayalso comprise an ECG analyzer for generating an amplitude spectrum area(AMSA) value using the transform. The patient treatment module may inturn be programmed to determine whether a prior defibrillation shock wasat least partially successful, and based at least in part on thedetermination of whether the prior defibrillation was at least partiallysuccessful, modifying a calculation of the likelihood of success fromdelivering the future defibrillating shock.

In yet other aspects, determining a likelihood of success fromdelivering a future defibrillating shock to the person depends on adetermination of whether one or more prior shocks delivered to theperson were successful in defibrillating the person. The mathematicaltransform can be selected from a group consisting of Fourier, discreteFourier, Hilbert, discrete Hilbert, wavelet, and discrete waveletmethods. In addition, the patient treatment module can be programmed todetermine the likelihood of success from delivering a futuredefibrillating shock using at least one patient-dependent physicalparameter separate from a patient ECG reading, and can be programmed todetermine the likelihood of success from delivering a futuredefibrillating shock using a measure of trans-thoracic impedance of theperson.

In yet another implementation, a method for managing care of a person isdisclosed. The method comprises monitoring, with an externaldefibrillator, electrocardiogram (ECG) data from a person receivingemergency cardiac assistance, the ECG data defining a vector frommultiple ECG signals; performing a vectorized mathematical transform ofthe ECG data that defines the vector from a time domain to a frequencydomain using a window in the time domain; determining a likelihood offuture defibrillation shock success using at least the mathematicaltransformation; and affecting control of the external defibrillatorbased on the identification of whether a present defibrillation shockwill likely be effective. The mathematical computation can comprise oneor more vectorized Fast Fourier Transforms (FFTs), and one or moreamplitude spectrum area (AMSA) calculations. The method can also includeapplying a pre-transform to the plurality of signals before applying themathematical computation so as to make the plurality of signalsorthogonal or near orthogonal, where the pre-transform can be applied inresponse to determining that the plurality of signals were notpreviously orthogonal or near orthogonal.

In some aspects, the method includes applying the mathematicalcomputation to the vector value by calculating FFT for each of theplurality of signals to create processed values, and then combining theprocessed values. Also, combining the processed values can includedetermining a root of a sum of the processed values. The mathematicalcomputation can also comprise a mathematical transform from a timedomain to a frequency domain on a window of data, including a window ofECG data. The window can comprises a tapered window and a Tukey window,can be between about one second and about 2 seconds wide, and can beselected from a group consisting of Tukey, Hann, Blackman-Harris, andFlat Top. Also, the mathematical transform can comprise a Fast FourierTransform.

In yet other aspects, determining a likelihood of future defibrillationshock success can comprise determining a value that is a function ofelectrocardiogram amplitude at particular different frequencies orfrequency ranges, determining an amplitude spectrum area (AMSA) valuefor the ECG data, and/or adjusting the determined AMSA value usinginformation about a prior defibrillation shock. The method can alsoinclude determining whether the adjusted AMSA value exceeds apredetermined threshold value. In addition, the method can includeproviding to a rescuer a visual, audible, or tactile alert that ashockable situation exists for the person receiving emergency cardiacassistance, if the adjusted AMSA value is determined to exceed thepredetermined threshold value. The method can also comprise determiningwhether a prior defibrillation shock was at least partially successful,and based at least in part on the determination of whether the priordefibrillation was at least partially successful, modifying acalculation of the likelihood of success from delivering the futuredefibrillating shock.

In certain aspects, determining a likelihood of success from deliveringa future defibrillating shock comprises performing a calculation by anoperation selected form a group consisting of logistic regression, tablelook-up, neural network, and fuzzy logic, and the likelihood can bedetermined using at least one patient-dependent physical parameterseparate from a patient ECG reading. The additional patient-dependentparameter can comprise an indication of trans-thoracic impedance of theperson receiving emergency cardiac care, and the indication oftrans-thoracic impedance can be determined from signals sensed by aplurality of electrocardiogram leads that also provide the EGO data.Also, under the method, the actions of monitoring, determining,identifying and affecting the control can be cyclically repeated.

In yet other aspects, the method also includes identifying compressiondepth of chest compressions performed on the person receiving emergencycardiac assistance, using a device on the person's sternum and incommunication with the external defibrillator, and providing feedback toa rescuer performing the chest compressions, the feedback regarding rateof compression, depth of compression, or both. Also, affecting controlof the defibrillator can include preventing a user from delivering ashock unless the determination of whether a shock will be effectiveexceeds a determined likelihood level, and displaying, to a user, anindicator of the determined indication of whether a shock will beeffective. Displaying the indicator can comprise displaying a value, ofmultiple possible values in a range that indicates a likelihood ofsuccess.

Other features and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a conceptual diagram showing the use of vectorized AMSAvalues to determine a likelihood that a shock will successfullydefibrillate a patient.

FIG. 1B shows schematically the combination of various types of data inmaking a determination about likely effectiveness of a defibrillatingshock.

FIG. 1C shows a victim of a cardiac event being treated with a portabledefibrillator.

FIG. 1D is a graph that represents changes in AMSA during an eventcorrelated to phases in the event.

FIG. 2 is a block diagram that shows a defibrillator with an electrodepackage and compression puck.

FIG. 3A is a flow chart of a process for providing a user with feedbackregarding a likelihood that a defibrillating shock will be successful.

FIG. 3B is a flow chart of a process for identifying a phase in acardiac event so as to provide guidance to a rescuer.

FIG. 3C is a flow chart of a process for using ECG vectors to determinelikelihood of success for defibrillating a patient.

FIGS. 4A and 4B are graphs showing relationships between sensitivity andspecificity, and AMSA threshold values for groups of patients.

FIG. 5 illustrates a defibrillator showing certain types of informationthat can be displayed to a rescuer.

FIG. 6 shows a general computer system that can provide interactivitywith a user of a medical device, such as feedback to a user in theperformance of CPR.

DETAILED DESCRIPTION

In general, defibrillation is a common treatment for variousarrhythmias, such as ventricular fibrillation (VF). However, there canbe undesired side effects (e.g., heart tissue damage, skin burns, etc.)that follow an electrical shock. Other undesired side effects includeunnecessary interruptions of chest compressions when a shock needs to bedelivered. Added to this, the effectiveness of defibrillation can fallgenerally over the elapsed time of a VF episode—where an episode may bemeasured from the time when a victim first starts feeling symptoms ofcardiac arrest or loses consciousness and falls down. (Generally, thetime from onset of a lethal VF episode and unconsciousness is relativelyshort, on the order of less than one-half minute.) It is thereforedesirable to predict whether defibrillation will be successful inrestoring a regular heartbeat following onset of an arrhythmic episode,and/or to determine how long it has been since a cardiac event startedor what stage of the event the patient is in (e.g., a first, second, orthird stage or phase).

Such predictions can each be referred to as an “indicator of success,” a“success indication,” or a determination and indication of a likelihoodof success, within the context of the present disclosure. The predictionmay cause a defibrillating shock to not be provided when the chance ofsuccessful defibrillation is low, and instead a system will wait untilthe chance of successful defibrillation increases to an acceptablelevel. Until such a time, a rescuer can be instructed to provide othercare such as regular chest compressions, forceful chest compressions, orother care.

As described below, the determination of a likelihood of success can beimproved by using vector values from ECG leads applied to a patient, andusing vectorized FFT to make an AMSA determination. Other factors mayalso be considered in combination with the determined AMSA value fordetermining a likelihood of success, such as trans-thoracic impedancemeasured for a patient.

Such a determination about likelihood of successful shock can be used toalter care in an automatic and/or manual manner. In an automatic manner,a defibrillator may be made incapable of delivering a shock unless asuccess indication is above a determined amount. In a manual manner, thesuccess indication may be shown to a rescuer, and the rescuer maydetermine whether to apply a shock or not based on the indication, orthe system may provide other information to the rescuer. For example,the indication of success may show a percentage likelihood that a shockwill succeed, or may be a less specific indicator, such as an indicationof which phase (e.g., of three phases discussed above and below) thevictim is currently in, so that the rescuer can immediately understand,from experience and training related to those phases, thatdefibrillation attempts are likely to be successful or not.

Additional information provided to a rescuer may take the form ofinstructions, such as instructions to perform chest compressions or someother action, where the action is selected from among a plurality ofpossible treatments based on the current phase for the victim. A systemmay also integrate both automatic and manual approaches—e.g., lockingout the ability to provide a shock until a threshold level is reached,and then showing the relative likelihood of success above that value.The likelihood of success can be shown in various manners, such as byshowing an actual percentage, or showing two or more of a low, medium,or high likelihood of success, e.g., on an electronic display of adefibrillator.

In certain implementations described herein, the present disclosure isdirected to systems and methods for predicting whether defibrillationwill be effective using amplitude spectrum area (AMSA) or any otherappropriate Shock Prediction Algorithms (SPA) using analysis of ECG data(including in a vector format), and adjusting such SPA predictions basedon either the existence of prior defibrillation shocks as well asobservations of a patient's reaction to those defibrillating shocks. Inparticular, it has been observed that victims of cardiac fibrillationwill successfully defibrillate for lower AMSA threshold values if theyhave been previously successfully defibrillated during the same rescuesession. Thus, rather than treating each shock as a discrete event inanalyzing the probability of success, the techniques described here cantake into account prior shock deliveries, and an observed response ofthe patient to those deliveries, in determining an AMSA value or othervalue that will indicate that a shock currently applied to the patientwill likely be successful (or not) in defibrillating the patient. Such adetermination may also be combined with determinations abouttrans-thoracic impedance (trans-thoracic impedance) of the patient, orother measured factors, as discussed more fully below.

To obtain better predictive value for the AMSA values, the time windowfrom which the ECG data for an AMSA determination is taken may be maderelative small (e.g., between 3 and 4 seconds, between 2 and 3 seconds,and between 1 and 2 seconds), which will place the data as close to thecurrent status of the patient as possible. Smaller windows may sufferfrom edge effects more-so than would larger windows, so the shape andcoefficients for the windows may also be selected to maximize predictivepower of the method. For example, a Tukey window having appropriatecoefficients may be employed, and the measurements may be made acrossmultiple scalar lead values with the data being processed as a vectorrepresentation of those scalar values.

FIG. 1A is a conceptual diagram showing the use of vectorized AMSAvalues to determine a likelihood that a shock will successfullydefibrillate a patient. In the scenario 100 shown in FIG. 1A, vectorvalues are acquired from electrodes and leads applied to the patient102. Those values are then processed, potentially with other data, toidentify a likelihood of shock success (perhaps along with otherfactors) and to present some indication of that likelihood to a rescuer.A series of lettered boxes shown adjacent to the patient 102 representparticular actions that a medical device, such as a portabledefibrillator, make take in providing such functionality and aredescribed in more detail below.

The patient 102 is shown as being physically connected to defibrillator104 at box (A) by a plurality of electrode leads. Though leads may referto individual conductors, the term will generally be used here to referto a pair of conductors that together provide a voltage signal to thedefibrillator. A typical 12-lead set-up may be used in particularimplementations.

The electrodes are connected to the patient 102 so that the electricaldepolarization signal generated by the beating heart arrives atdifferent ones of the leads in a substantially orthogonal manner. Inparticular, the heart tissue depolarizes in a wave that defines thecoordinated beat of the heart, so that electrical potential sensed byelectrodes follows that order of depolarization to zones on thepatient's skin that are located closest to the depolarizing zone. Thus,by placing the electrodes in appropriate locations, the phases of therelevant signals can be made near orthogonal (e.g., leading or trailingby about 90 degrees in the cycle) or out of phase (180 degrees from eachother in the cycle). For example, leads may receive signals in afront-back configuration on the patient's 102 torso, or side-side, whereeach pair can be 90 degrees from another pair. Such placement may beachieved even for untrained users by placing the electrodes on a singlesheet that might also include electrodes for shocking the patient, andthat, when placed on the patient's torso, results in the sensingelectrodes being at such appropriate positions. Alternatively, or inaddition, graphics may be placed on the electrodes that show properelectrode placement.

The number of leads providing signals to the defibrillator 104 can vary,though two or more are generally needed in order to obtain a vectorizedsignal. The signals provided by two such leads are shown schematicallyin the box labeled (A), as multiple vectors unassociated with eachother, and at box (b) as vectors represented having a common base, andeach having a particular magnitude and direction at a particular pointin time in a cardiac cycle. As shown, the signals from the leads aresubstantially, though not totally, orthogonal to each other, where aparticular angle may be selected to be a zero degree point for thecardiac cycle (i.e., for each heartbeat).

At the box labeled (B), the defibrillator 104 has adjusted the signalsto make them orthogonal to each by known transformation techniques tocreate an XYZ representation for the patient's 102 signal. For exampleparticular signals may be projected onto orthogonal vectorrepresentations in an XYZ representation. The representation thus shows,in an orthogonal manner, the temporal change in surface potentials forthe heart in a manner that is more readily susceptible to analysis andcomparison to prior analysis of prior cardiac events. Use of a vectorrepresentation may permit better visibility into the condition of theheart during VF, when the heart's motion and electrical activity is notorganized. For example, the analysis here may provide a betterindication of the actual physical qualities of the heart during VF, asopposed to random changes that may have little or no helpfulinformation.

In some implementations, an optimal angle for projecting the values ontoan orthogonal representation can be determined. For example, adetermination may first be made to identify an angle for a projectionthat will provide a maximum amplitude of a projection for a particularsampling interval. A maximum amplitude can then be identified in theinterval from that angle. And the values may then be geometricalprojected onto the coordinates of the orthogonal representation. Foreach sampling interval then, the process selects a configuration thatprovides a maximum amplitude, so that the signal is normalized from onesample to the next.

At the box labeled (C), At the box labeled (C), the Complex DiscreteFourier Transform (DFT) is applied to all components of the producedvector to transform the data from the time domain to the frequencydomain. Such transform may occur in multi-dimensional space and producedata that is interpretable in a complex frequency spectrum. Suchtransformation may occur by standard mathematical transform methods andproduce data that is interpretable in standard manners.

At the box labeled (D), the frequency domain data is used to compute anAMSA value, again by familiar mechanisms. Here, the AMSA value is 16.2mV-Hz, though it could also be transformed to an equivalent value thatis expressed in a different manner, and still be considered an AMSAvalue. More generally, the computation may be of a value that representsa weighted amplitude of the signal, and here generated from the signalafter the FFT transformation so as to provide an indication of thesignal weight in the frequency domain.

At the box labeled (E), the AMSA value is combined with other factorsthat are known to be “signals” that affect the success rate ofdefibrillating shocks applied to a patient. For example, considerationsabout how long the patient has been in cardiac arrest, how many priorshocks have been applied, the relative success level of those priorshocks (e.g., did they fully defibrillate or partially defibrillate, andfor how long?), trans-thoracic impedance, and other possible inputs.Such values may be normalized to a common representation (e.g., someparameter-less number) and weighted according to what their respectivecontribution is determined to be for predicting a likelihood that ashock will be successful. Each of these input signals may be convertedinto a common format for all the signals (e.g., a particulardimensionless value) and as part of that process or as an additionalstep, each signal may be weighted so that a composite indication can begenerated that properly incorporates each relevant signal at a levelthat such signal contributes to the likelihood that a shock will succeedor not succeed.

At the box labeled (F), the computation from such a combination (or fromthe AMSA or other weighted amplitude value alone) may be turned into amore human-understandable representation. As shown in the figure, thatrepresentation is expressed as a percentage likelihood of success. Thatnumber may then be displayed on a screen of the defibrillator 104 orotherwise communicated to a rescuer, such as audibly or via a screen ondisplay on electronic glasses worn by the rescuer. The rescuer may usesuch information to determine the advisability of providing the shock.For example, an ambulance service may train its EMTs to only shock whenthe value is higher than Y early in a rescue and higher than Z later ina rescue or after unsuccessful shocks have been provided.

The likelihood may also be presented in other manners. For example,instead of or in addition to showing the percentage value, a series ofred, yellow, and green colors may be displayed (e.g., from a bulb, froman icon on the screen of the defibrillator 104, or in the color of thetext that shows the percentage) to represent low likelihood of success(and potential physical lock-out by the device of its ability to delivera shock), medium likelihood of success, or high likelihood of success,respectively. Generally, the more complex representations would not beshown to lay rescuers (e.g., using AED devices) because the informationcould overwhelm them in an already-overwhelming situation.

In actual implementation, the received ECG-related signals will bechanging constantly and cyclicly with each cycle of the beating of thepatient's heart. The signals will be sampled at a particular rate (e.g.,many times per second) and the likelihood calculation can be made over awindow of time for each such sampling, for every nth sampling, or formultiple samplings as a group (where each reading can serve as an inputto an averaging technique). A running average of the likelihood valueover multiple time windows can also be maintained, and the presentedindication may depend on that running average, so as to prevent largeand fast fluctuations in what is displayed to the rescuer who isoperating the defibrillator 104.

FIG. 1B shows schematically the combination of various types of data inmaking a determination about likely effectiveness of a defibrillatingshock. In a particular implementation, one of the types of data may beused alone, or multiple of the types may be combined so as to create acomposite likelihood—e.g., by giving a score to each type and a weight,and combining them all to generate a weighted composite score for alikelihood. In this example, a shock indication 116 is the outcome of adecision process that may be performed by a defibrillator alone or incombination with one or more pieces of ancillary equipment (e.g., acomputing device such as a smartphone carried by a healthcare provider).The shock indication 116 can be provided to part of the defibrillator,e.g., via an analog or digital signal that represents the indication, sothat the part of the defibrillator may cause a shock feature to beexecuted or to cause it to be enabled so that it can be manuallyexecuted by an operator of the defibrillator. The shock indication mayalso or alternatively be provided to the rescuer so as to indicate thatthe rescuer can or should cause a defibrillating shock to be delivered.(In the context of this disclosure, a defibrillating shock is one of alevel designed to cause defibrillation, but it does not need to besuccessful in causing the defibrillation.)

The relevant inputs may obtain at least some of their data from signalsgenerated by a pair of electrodes 103 that may be adhered to a patient'storso-above one breast and below the other, for example, in a typicalmanner. The electrodes may include leads for obtaining ECG data (e.g.,via a 12-lead arrangement) and providing such data for analysis for anumber of purposes. In addition, a CPR puck 105 may be placed on apatient's sternum and may deliver signals indicative of acceleration ofthe puck, and thus of up-down acceleration of the patient's sternum,which may be mathematically integrated so as to identify a depth ofcompression by the rescuer (and can also be used more simply to identifywhether the patient is currently receiving chest compressions or not).

The electrodes 103 may be electrically connected to an ECG unit 106,which may be part of a portable defibrillator and may combine data fromdifferent leads (e.g., 8 or 12 leads) in a familiar manner to constructa signal that is representative of the patient's ECG pattern. The ECGcombination may also be represented mathematically as a vector value,such as including vector components in an XYZ representation. Such anECG signal is often used to generate a visual representation of thepatient's ECG pattern on a screen of the defibrillator. The ECG-relateddata may also be analyzed in various ways to learn about the currentcondition of the patient, including in determining what sort of shockindication to provide to control the defibrillator or to display to arescuer.

As one such example, ECG data may be provided to an AMSA analyzer 108,which may nearly continuously and repeatedly compute an AMSA number orsimilar indicator that represents ECG amplitude at particular differentfrequencies and/or frequency ranges in an aggregated form (e.g., anumeral that represents a value of the amplitude across thefrequencies). Other aspects of the ECG reading may be similarly used forsuch analysis, and may include weighting and weighting across amplitudesof values in a range. Generally, the goal is to identify a waveform inwhich amplitude of the VF signals is large, and in particular,relatively large in the higher frequency ranges. Similarly, powerspectrum area can be measured and its value can be used as an input thatis alternative to, or in addition to, an AMSA value for purposes ofmaking a shock indication.

As described in more detail above and below, a current AMSA value (or acombination of multiple values over a short period taken in differentwindows of time) can be used to determine whether a shock is likely tobe successful, and a plurality of combined AMSA values, such as arunning average computed many times over time (and each covering a timeperiod longer than the time period for the first AMSA value) using amoving window may indicate how much time has elapsed since a cardiacevent began and thus indicate which phase, of multiple phases during aVF event, the victim is in, where each phase calls for a differentmost-effective treatment sub-protocol. Also, when rescuers first arriveon a scene, several seconds of ECG data may be used to provide them aninitial indication of the time since the event started and/or the phasein which the victim currently is in—e.g., by displaying a number ofelapsed minutes or the name of one of multiple phases (like the threephases discussed above) on a display screen of a medical device such asa monitor or defibrillator/monitor.

The AMSA analyzer 108 may be programmed to perform the analysis of theECG inputs, and perhaps other inputs, so as to maximize the predictivevalue of the AMSA readings, whether by affecting inputs to the AMSAdetermination, and/or making an AMSA determination and then adjustingthe AMSA value that is generated from that determination. As oneexample, the size of the window in time from which ECG data is taken inmaking the calculation may be set to maximize the predictive value, suchas by being about 1 second to about 1.5 seconds long. As anotherexample, the shape of the window may be tapered, such as by being in theform of a Tukey or Hann window, rather than having vertical edges like aboxcar window. Similarly, the coefficients for the window, such as Chi2and p may be set to maximize the expected predictive value of thecalculation. The AMSA analyzer may also be programmed to change suchvalues dynamically over the course of a particular VF incident, eitherby moving the values progressively as time elapses so as to make thevalues match known expected values for maximizing the predictive effectof the calculation, or to respond to particular readings, e.g., to useparticular window length, form, or coefficients when an AMSA value is ina certain defined range.

The predictive quality of the AMSA determination may also be increasedby performing the FFT or other transform in making the calculation on avector value rather than a scalar value from the leads. Such an approachmay provide a more complete picture of the operation of the heart, suchas by catching minimums and maximums in the various signals morereliably and in capturing a picture of a greater portion of the heartrather than a particular point on the heart, where such point might beless representative of the overall condition of the heart. The overallprocess may thus better represent the actual condition of the heart,rather the non-indicative random changes in the signals.

A trans-thoracic impedance module 110 may also obtain information fromsensors provided with the electrodes 103, which indicates the impedanceof the patient between the locations of the two electrodes. Theimpedance can also be a factor in determining a shock indication asdescribed in more detail below.

A defibrillation history success module 112 tracks the application ofdefibrillating shocks to the patient and whether they were successful indefibrillating the patient, and/or the level to which they weresuccessful. For example, the module 112 may monitor the ECG waveform intime windows of various sizes for a rhythm that matches a profile of a“normal” heart rhythm, and if the normal rhythm is determined to beestablished for a predetermined time period after the application of adefibrillating shock, the module 112 may register the existence of asuccessful shock. If a shock is applied and a normal rhythm is notestablished within a time window after the delivery of the shock, themodule 112 can register a failed shock. In addition to registering abinary value of success/fail, the module may further analyze the ECGsignal to determine the level of the success or failure and may, forexample, assign a score to the chance of success of each shock, such asa normalized score between 0 (no chance of success) and 1 (absolutecertainty).

A CPR chest compression module 114 may receive signals about the motionof the puck 105 to determine whether chest compressions are currentlybeing applied to the patient, and to determine the depth of suchcompressions. Such information may be used, for example, in giving arescuer feedback about the pace and depth of the chest compressions(e.g., the defibrillator could generate a voice that says “pushharder”). The presence of current chest compression activity may alsosignal the other components that a shock is not currently advisable, orthat ECG data should be analyzed in a particular manner so as to removeresidual artifacts in the ECG signal from the activity of the chestcompressions.

Information about pharmacological agents 115 provided to a patient mayalso be identified and taken into account in providing a shockindication to a rescuer. Such information may be obtained manually, suchas by a rescuer entering, via a screen on a defibrillator or on a tabletcomputer that communicates with the defibrillator, identifiers for thetype of agent administered to a patient, the time of administration, andthe amount administered. The information may also be obtainedautomatically, such as from instruments used to administer theparticular pharmacological agents. The device that provides a shockindication may also take that information into account in identifyingthe likelihood that a shock will be successful if provided to thepatient (e.g., by shifting up or down an AMSA threshold for measuringshock success likelihood), and for other relevant purposes.

One or more of the particular factors discussed here may then be fed toa shock indication module 116, which may combine them each according toan appropriate formula so as to generate a binary or analog shockindication. For example, any of the following appropriate steps may betaken: a score may be generated for each of the factors, the scores maynormalized (e.g., to a 0 to 1 or 0 to 101 scale), a weighting may beapplied to each of the scores to represent a determined relevance ofthat factor to the predictability of a shock outcome, the scores may betotaled or otherwise combined, and an indication can be determined suchas a go/no go indication, a percentage of likely success, and other suchindications.

In this manner then, the system 101 may take into account one or aplurality of factors in determining whether a shock to be delivered to apatient is likely to be successful. The factors may take data measuredform a plurality of different inputs (e.g., ECG, trans-thoracicimpedance, delivered agents, etc.), and may be combined to create alikelihood indication, such as a numerical score that is to be measuredagainst a predetermined scale (e.g., 0 to 101% likelihood or A to Fgrade). Such determination may then be used to control anautomatically-operated system (e.g., that delivers chest compressionsmechanically), to limit operation of a manually-operated system (e.g.,by enabling a shock that is triggered by a user pressing a button), orby simply providing information to a system whose shock is determinedsolely by a rescuer (e.g., for manual defibrillators in which theoperator is a well-trained professional).

FIG. 1C shows a victim 122 of cardiac arrest being cared for by arescuer and a defibrillator 124. The defibrillator 124 includes anelectrode package 126 and a compression puck 128 generally coupledthereto. Examples of a portable defibrillator that can be used tomonitor and deliver a shock to a patient include the AED PLUS automatedexternal defibrillator or the AED PRO automated external defibrillator,both from ZOLL Medical Corporation of Chelmsford, Mass. Otherembodiments of the defibrillator 124 are possible.

In the pictured example, the victim 122 is rendered prone due to anarrhythmic episode, and the electrode package 126 and the compressionpuck 128 are positioned on the torso of the victim 122 in an appropriateand known arrangement. In accordance with the present disclosure, thedefibrillator 124, in tandem with one or both of the electrode package126 and the compression puck 128, is configured to determine whether adefibrillation shock will be an effective measure to terminate thearrhythmic episode. The determination is generally based on priorsuccess or failure of defibrillating shocks, one or more trans-thoracicimpedance measurements, and one or more calculated AMSA values. As shownin the figure, the victim 122 is shown at two points in time—(a) pointt1 at which the patient has been defibrillated and is shown with hiseyes open and a healthy ECG pattern 135A to indicate such successfuldefibrillation, and (b) at a later time t2, when the patient hasrefibrillated and is shown with closed eyes to represent such a state,and with an erratic ECG trace 135B. (Of course, there would also be atime before t1 when the patient was previously suffering from VF.)

The defibrillator 124 is configured to acquire and manipulate both atrans-thoracic impedance signal 130 and an ECG signal 132 via theelectrode package. As described in further detail below, atrans-thoracic impedance measurement (S2) is a parameter derived fromthe trans-thoracic impedance signal 130 that represents, among otherthings, thoracic fluid content. An AMSA value (V-Hz) is a parametercalculated by integrating the Fourier transform of the ECG signal 132over a finite frequency range. The AMSA value is one form of calculationthat represents a value of an ECG signal from a victim, while other SPAvalues may likewise be computed.

The defibrillator 124 is further configured to display an indicator134A/B based on the defibrillating history (determined from ECG data),trans-thoracic impedance measurement(s) and AMSA value(s) obtained fromthe ECG signal 132, trans-thoracic impedance signal 130 and an ECGsignal 132, respectively. The indicator 134A/B generally provides aperceptible cue that suggests whether or not a particular defibrillationevent will likely terminate the arrhythmic episode of the victim 122.For example, for the victim 122 at time t1, the indicator 134A displaysan X to indicate that no shock should be delivered to the victim 122. Incontrast, at time t2, the indicator 1348 displays a success indicationof “88%,” so a rescuer (not shown) can be instructed “Press to Shock,”so as to apply a shock to the victim 122 via actuation of a control 136(e.g., a button that the user can actuate).

In this situation, the indication of an 88% likelihood of success wasmade by consulting data structure 130, which may be stored in memory ofdefibrillator 124 upon analysis that occurred around the time of t1, andapplying an appropriate calculation to data from the data structure 130.In particular, the defibrillator may analyze ECG data and an indicatorprovided by shock delivery circuitry in order to determine that a shockwas delivered, and at a time soon after, the patient's heart rhythmentered a normal pattern, such that the defibrillator 124 may determinethat the shock was a success at time t1. Upon making such adetermination, the defibrillator may update data structure 130 toindicate that a successful defibrillation event has occurred during therescue attempt. Other shocks may also be delivered, and the datastructure 130 may be updated to reflect such events, and the success orfailure of such events.

Also, the ECG signal 132 may be made up of multiple separate signalstaken by different ones of the leads applied to the victim 122. Suchmultiple signals may be used to construct one or more vectorized signalsfrom the victim 122. The computations performed on those signals (e.g.,FFT transforms) may also be performed in vectorized form so as toproduce an AMSA or similar value, in manners like those discussed aboveand below.

Data structure 130 or another data structure may also store informationabout prior AMSA readings for the victim during the particular VFepisode. For example, a separate AMSA measurement and calculation may bemade periodically (e.g., multiple times each second, once each second,or once every several seconds) and at least some past calculated AMSAvalues may be stored in data structure 130. Such values may be combined,and determinations may be made about general values (with lowvariability because of the combining) and trends in AMSA values, wheresuch determination may indicate information such as the progress of thevictim through phases that are generally indicative of the likelihood ofsuccess of particular actions taken on the victim by a rescuer.Moreover, such information may be used to generate an indication to arescuer of the elapsed time (approximate) since the victim entered VF,or an indication of the phase the victim is currently in, among otherthings.

Embodiments other than those that display a percentage likelihood for ashock indication are possible for the one likelihood indicationdiscussed here. For example, it will be appreciated that a successindication may be implemented as any appropriate type of perceptiblefeedback (e.g., haptic, audio, etc.) as desired. Two simultaneousindications may also be provided, where both may be the same style ofindication (e.g., visual display) or different types (e.g., visualdisplay for one and haptic for the other)—e.g., the phase in which avictim is currently located may be displayed on a screen of adefibrillator, while a current AMSA value indicating a relatively highchance of success may be communicated by vibration of or display on apuck on which the rescuer has placed his hands (so as to encourage therescuer to back-off and provide the shock).

In certain implementations, the defibrillator 124 may make thedetermination of a likelihood of success without expressly notifying therescuer, and may simply use the determination to determine when to tellthe rescuer that a shock may be delivered, or to provide otherinstructions to a rescuer. In other situations, the defibrillator 124may explicitly indicate the likelihood of success, such as by showing apercentage likelihood, by showing less discrete gradiations for success(e.g., poor, good, very good, and excellent), or by displaying a rangeof colors (e.g., with red indicating a poor chance and green indicatinga good chance). The type of indication that is displayed may also differbased on a mode in which the defibrillator 124 is operating—for example,in a professional mode, more detailed information may be provided,whereas in an AED mode, simpler information (a “go”/“no go” choice) maybe presented.

In such manner then, the defibrillator may conduct a number ofrelatively complex calculations and may combine multiple factors indetermining whether to allow a shock to be provided to a patient, or toencourage the application of such a shock by a rescuer.

FIG. 1D is a graph 130 that represents changes in AMSA during a VF eventcorrelated to phases in the event. In general, the graph 130 shows howAMSA varies along with variations in a patient ECG, and varies moregenerally over a longer time period by falling over time after the eventhas started. Such AMSA values may be computed from vectorized ECG datain the manners described above.

The time across this graph may be, for example, about 15 minutes. Thetime is broken into three phases. A defibrillation phase 132 mayrepresent about the first 4 minutes (plus or minus one minute) of theevent. A deep CPR phase 131 may run from about four minutes to about 10minutes after onset of the event. And an Other CPR phase 137 mayrepresent the remainder of the event, assuming the victim has not beenrevived by that time. Each of these time periods corresponds to aparticular phase in the patient's condition that may in turn correspondto a different manner in which the patient should best be treated by arescuer

Line 138 is represented as being drawn through all of the AMSA valuescomputed periodically throughout the time of the event. (The line isshown falling linearly here for clarity, though AMSA generally decreasesexponentially. If AMSA were graphed for a rescuer, it could be shown asan exponential curve, as a line on an exponential scale, and/or witherror bars showing statistical variation in the readings.) As can beseen, the AMSA values vary up and down (with a general downward trendover time), and such variation represents changes in the victim's ECGwhere the changes can represent changes in likelihood that a shock,currently delivered, will be successful. But although there isrelatively large variation over short time periods, the variation isless over longer time windows, such as over 10 or more seconds. Thus,for example, AMSA values may be computed periodically over a short timeperiod, and more general values may be computed by averaging orotherwise combining the individual measurements. A running average isrepresented by line 140. Line 140 may simply represent the average ofpast computations, and may also be extended into the future in certainimplementations, such as by linear regression or other appropriatestatistical techniques. For purposes of clarity, the overall AMSA valueis shown here as falling linearly with time, though the actual variationmay differ from what is shown here.

In this example, two points on line 140 are particularly relevant,points 142 and 144. These points represent locations at which thecombined AMSA value measurement (e.g., averaged over a window of time)fall below a predetermined value. For example, the value for point 142may have been selected from observations of ECG data, and correspondingAMSA values from data captured for actual real-world resuscitationevents with real victims, and such data may indicate that resuscitationfrom shock falls below an acceptable value and/or falls off more quicklyupon passing below a particular AMSA value. Such AMSA value may beselected as a cut-off point that defines the line between the firstphase and the second phase. Similarly, such data may indicate that chestcompressions or a particular type of chest compressions, such asforceful chest compressions, fell below a particular level ofeffectiveness or changed relatively rapidly in their effectiveness pastanother AMSA value. As such, point 144 may represent an AMSA valuedetermined from such data analysis to correspond to such changes asobserved across the large population of VF events. The points 142, 144are mapped to the determined values with horizontal dotted lines, and adefibrillator or other device may monitor the combined AMSA value as anevent progresses so as to identify when the predetermined AMSA value isreached. A similar monitoring may be employed with respect toidentifying the existence of point 144.

Each of the points 142, 144 is also mapped to the time axis,representing the time at which the particular victim was determined tohave transitioned from one phase to another. Generally, the times willbe relatively similar as between different victims and different cardiacevents, where the changes are driven in large part by ischemic effectsthat the event has on the heart tissue. At such points in time for theparticular victim represented by this graph, the behavior of a medicaldevice such as a defibrillator that is treating the victim may change inthe ways discussed above and below.

As such, the device may determine an estimated time since the VF eventbegan using AMSA values and/or other information, where particular AMSAvalues from a studied population have been determined to correspond tocertain times since collapse or other instantiation of the VF event.Such information may be displayed in real-time or stored, such as todetermine response times, and to perform studies on effectiveness ofrescuers as a function of the time since initiation of the event when adefibrillator is first connected and operable for the victim.

Example

As for particular AMSA values for use in defining points 142 and 144,one example may be instructive. Data from an Utstein-compliant registryalong with electronic ECG records were collected on consecutive adultnon-traumatic OHCA patients treated by 2 EMS agencies over a 2 yearperiod. Patients with bystander witnessed CA and with VF as initial CArhythm were included (n=41). AMSA was calculated in earliest pausewithout compression artifacts, using a 2 second ECG with a Tukey (0.2)FFT window. VF duration was calculated as the sum of the time intervalfrom collapse to defibrillator on and the time interval fromdefibrillator on to first CPR interruption for defibrillation delivery.

VF duration ranged between 6.5 and 29.6 min (11.3+4.1 min), with acorresponding AMSA between 2.1 and 16.4 mV-Hz (9.4+4.2 mV-Hz). AMSAmeasured in the circulatory phase (N=19) was significantly higher thanthat in the metabolic phase (N=22) (8.14+3.17 vs. 5.98+2.88, p=0.03).Linear regression revealed that AMSA decreased in the analyzedpopulation by 0.22 mV-Hz for every min of VF. AMSA was able to predictcirculatory phase with an accuracy of 0.7 in ROC area. An AMSA thresholdof 10 mV-Hz was able to predict the circulatory phase with sensitivityof 32%, specificity of 95%, PPV of 86%, NPV of 62%, and overall accuracyof 66%.

Referring now to FIG. 2, a schematic block diagram 200 shows an exampledefibrillator 201, along with the example electrode package 103 andcompression puck 105, of FIG. 1A in more detail. In general, thedefibrillator 201, and optionally one or more of the electrode package103 and compression puck 105, defines an apparatus for administeringcare to a patient, subject, or individual (e.g., patient 102) whorequires cardiac assistance.

The defibrillator 201 includes a switch 202 and at least one capacitor204 for selectively supplying or applying a shock to a subject. Thedefibrillator 201 further includes an ECG analyzer module 206, atrans-thoracic impedance module 208, a CPR feedback module 210 thatcontrols frequency and magnitude of chest compressions applied to asubject, a patient treatment (PT) module 212 (which includes adefibrillation history analyzer 215), a speaker 214, and a display 216.In this example, the ECG analyzer module 206, trans-thoracic impedancemodule 208, CPR feedback module 210, and patient treatment (PT) module212 are grouped together as a logical module 218, which may beimplemented by one or more computer processors. For example, respectiveelements of the logical module 218 can be implemented as: (i) a sequenceof computer implemented instructions executing on at least one computerprocessor of the defibrillator 201; and (ii) interconnected logic orhardware modules within the defibrillator 201, as described in furtherdetail below in connection with FIG. 6.

In the example of FIG. 2, the electrode package 103 is connected to theswitch 202 via port on the defibrillator 201 so that different packagesmay be connected at different times. The electrode package 103 may alsobe connected through the port to ECG analyzer module 206, andtrans-thoracic impedance module 208. The electrode package 103 includeselectrodes for delivering a delivering a defibrillating electrical pulseto a patient in addition to capturing electrical signals from the heartthat indicate ECG functioning. In this example there are a plurality ofphysical and signal (pairs of physical) leads so that vectorrepresentations of the ECG data may be collected and processed.

The compression puck 105 is connected, in this example, to the CPRfeedback module 210. In one embodiment, the ECG analyzer module 206 is acomponent that receives an ECG signal. Similarly, the trans-thoracicimpedance module 208 is a component that receives transthoracicimpedance (e.g., trans-thoracic impedance signal 110). Other embodimentsare also possible.

The patient treatment module 212 is configured to receive an input fromeach one of the ECG analyzer module 206, trans-thoracic impedance module208, and CPR feedback module 210. The patient treatment module 212 usesinputs as received from at least the ECG analyzer module 206 andtrans-thoracic impedance module 208 to predict whether a defibrillationevent will likely terminate an arrhythmic episode. For example, ECG datacan be used both to determine AMSA values for a patient (including viathe vectorized methods described above and below), and also determinewhether shocks are effective or not so that such information can besaved and used to identify likelihoods that subsequent shocks will beeffective). In this manner, the patient treatment module 212 usesinformation derived from both an ECG signal (both for AMSA and foradjusting the AMSA value) and transthoracic impedance measurement toprovide a determination of a likelihood of success for delivering adefibrillating shock to a subject.

The patient treatment module 212 is further configured to provide aninput to each one of the speaker 214, display 216, and switch 202. Ingeneral, input provided to the speaker 214 and a display 216 correspondsto either a success indication or a failure indication regarding thelikelihood of success for delivering a shock to the subject. In oneembodiment, the difference between a success indication and a failureindication is binary and based on a threshold. For example, a successindication may be relayed to the display 216 when the chancescorresponding to a successful defibrillation event is greater than 75%.In this example, the value “75%” may be rendered on the display 216indicating a positive likelihood of success. When a positive likelihoodof success is indicated, the patient treatment module 212 enables theswitch 202 such that a shock may be delivered to a subject.

The patient treatment module 212 may also implement an ECG analyzer forgenerating an indication of heart rate for the patent, for generating anindication of heart rate variability for the patent, an indication ofECG amplitude for the patent, and/or an indication of a first or secondderivative of ECG amplitude for the patient. The indication of ECGamplitude can include an RMS measurement, measured peak-to-peak,peak-to-trough, or an average of peak-to-peak or peak-to-trough over aspecified interval. Such indications obtained by the ECG analyzer may beprovided to compute an AMSA value for the patient and/or can be used incombination with a computed AMSA value so as to generate some derivativeindication regarding whether a subsequent shock is likely or unlikely tobe effective (and the degree, e.g., along a percentage scale, of thelikelihood).

In another embodiment, likelihood of a successful defibrillation eventmay be classified into one of many possible groups such as, for example,low, medium, and high likelihood of success. With a “low” likelihood ofsuccess (e.g., corresponding to a successful defibrillation event isless than 50%), the patient treatment module 212 disables the switch 202such that a shock cannot be delivered to a subject. With a “medium”likelihood of success (e.g., corresponding to a successfuldefibrillation event is greater than 50% but less than 75%), the patienttreatment module 212 enables the switch 202 such that a shock may bedelivered to a subject, but also renders a warning on the display 216that the likelihood of success is questionable. With a “high” likelihoodof success (e.g., corresponding to a successful defibrillation event isgreater than or equal to 75%), the patient treatment module 212 enablesthe switch 202 such that a shock may be delivered to a subject, and alsorenders a cue on the display 216 indicating that the likelihood ofsuccess is very good. Still other embodiments are possible.

Thus, the system 200 may provide, in a portable electric device (e.g., abattery-operated device) the capability to analyze a number of inputsand to identify a variety of factors from those inputs, where thefactors can then be combined to provide a flexible, intelligentdetermination of likely success.

Referring now to FIG. 3A, there is shown a process for identifying alikelihood that a defibrillating shock will be effective, using vectorinput. The process begins at box 300, where a patient is generallymonitored. The monitoring may take a familiar form and involve receivingsignals from a plurality of different leads connected to a monitor thatis part of a portable defibrillator. Other sensors can also bemonitored, including an accelerometer in a CPR puck on the patient'schest, the patient's blood pressure and pulse, the patient'stemperature, the patient's trans-thoracic impedance, and other relevantpatient-related parameters.

At box 302, ECG scalar signals are identified from the sensed signals.In particular, signals from a plurality of leads (paired physical leads)can be captured using moving windows that scan across the incomingsignals, and the signal may be digitized in a familiar manner forprocessing in certain implementations. The various scalar signals may becaptured simultaneously for each of the leads and may be processedtogether.

At box 304, the signals are aligned so that they are orthogonal witheach other. In particular, the locations of the electrodes on thepatient may affect the relative phases of the different scalar signals.In this step, the scalar values may be adjusted so that they areorthogonal to each other or essentially orthogonal (e.g., where the lackof perfect orthogonality affects the predictive score by less than 5 or10 percent).

At box 306, a vector signal is formed. Such a signal may be formed viathe combination of two or more scalar values in a familiar manner, wherethe vector value has an amplitude and a direction, and where thedirection may be envisioned as rotating through a cycle in a vectorspace with each beat of the patient's heart. The vector value representsa contribution from readings at multiple locations on the patient'storso, and thus is able to capture more aspects of the ECG signal.

At box 308, an AMSA value is computed using the vector signal. Suchvectorized AMSA captures information from the multiple locations andindicates the amplitude of the spectrum area for those signals ascombined into a vector value. The value returned may be in a typicalAMSA form like those discussed above, or may be transformed into anequivalent value (and still be considered an AMSA value in that case).

At box 310, an indication of care for the patient is provided, wherethat indication is based at least in part on the computed AMSA value. Inparticular, the indication of care may include communicating to arescuer whether a defibrillating shock for the patient is advised. Suchcommunication may be made by indicating whether such a shock iscurrently enabled on a defibrillator and/or providing a value selectedfrom multiple values along a scale where the selected value indicates arelative likelihood of success, such as by an A to F grade, or apercentage likelihood of success.

The indication of care may also be based on factors in addition to AMSAor another SPA indication. For example, the indication may additionallydepend on the success or failure of prior shocks, the amount of time thepatient has been undergoing cardiac arrest, determinations ofpharmaceuticals that have been administered to the patient, thepatient's age and weight and gender, and other variables that have beendetermined to be relevant to the likelihood calculation. The particularsignals used in determining the likelihood, and the way it is presentedto a rescuer, may vary depending on whether the rescuer is lay or expert(e.g., as determined by whether the defibrillator is operating as an AEDor a professional defibrillator), such as by taking into account morefactors and providing more information to professional rescuers.

FIG. 3B shows an example method for administering care to an individualrequiring cardiac assistance. In one embodiment, the method isimplemented by the example defibrillators described above in connectionwith FIGS. 1A-D and 2. However, other embodiments are possible. Themethod is similar to that described for FIG. 3A, but focuses less on theprocessing of vector inputs, and more on the combining of variousdifferent signals for computing a likelihood of success for adefibrillating shock.

At box 320, at least one of an ECG signal and a trans-thoracic impedancesignal (e.g., trans-thoracic impedance signal 110) of the subjectreceiving cardiac care is monitored. In general, an individual receivingcardiac care includes the individual at any time during a cardiac event,including whether or not individual is receiving active care (e.g.,chest compressions). The ECG signal may be scalar in character or avector form that is a combination of multiple scalar signals.

At box 322, a trans-thoracic impedance value is extracted from thetrans-thoracic impedance signal as monitored at step 320. Additionally,at step 322, an AMSA value can be calculated from the ECG signal asmonitored at step 320 by integrating the Fourier transform (e.g., FFT)of the ECG signal over a finite frequency range. Example frequencycontent of an arrhythmic ECG signal generally ranges between about 1 Hzto about 40 Hz, with amplitude of about 50 mV or less. An example of anAMSA value calculated from such a signal ranges between about 5 mV-Hz toabout 20 mV-Hz. Also, values that are transformable to AMSA values ofthe form just given are considered to be AMSA values also for purposesof this document. It will be appreciated however that this is only anexample, and that the magnitude and spectra of an ECG signal rangesgreatly.

The AMSA value may be determined from a moving window (or moving windowsfor each scalar value that makes up a vector) that moves in time throughthe incoming ECG data as it arrives (e.g., the raw ECG data may becached for a period at least as long as the window), where the windowmay be about one second wide (or more), and can be measured multipletimes each second so that there are overlapping windows. The window mayalso have a tapered (rather than rectangular) window function so as toimprove the accuracy of the AMSA value in predicting defibrillationsuccess. Furthermore, the coefficients for the window may be selected tomaximize the predictive ability of the system. In addition, multipledifferent AMSA values may be determined (e.g., with different windowsize, type, and/or coefficients) and a most-accurate AMSA may bedetermined and used to make a prediction, or a composite value may begenerated from each of the determined AMSA values.

Additionally, the window size, type, and coefficients can change overtime to allow a system to dynamically adjust to a particular VF event.For example, using determinations about the phase in which a VF eventis, a system may change such parameters to switch to a window that isdetermined to better predict defibrillation success. Alternatively, ablend of window techniques may be used and the blend may change overtime, while a composite prediction score is determined from the blendedtechniques.

At box 324, the process identifies success levels of prior shocksapplied to the patient during the cardiac event. Such determination mayoccur in various manners. At a simplest level, the process may simplytrack the number of times a defibrillating shock has been provided tothe patient. In more complex implementations, the process may identifyhow many attempts were successful and how many were not, and in aslightly more complex implementation, may identify which were successfuland which were not (e.g., because subsequent steps may perform moreaccurately by weighting the influence of different ones of the priordefibrillations in different ways). In yet more complex systems, thedegrees of prior success can be determined, which may includedetermining how close the patient's defibrillated heart rate was to apredetermined rate (either a particular rate or a range of rates) or howconsistent the rate was over time, or a combination of both to generatea score for the quality of the defibrillation. Other examples ofphysiologic measure that may be useful for generating a score may bepulse oximetry, capnography, blood pressure, or other pulse or bloodflow detection methods.

As one such example, scoring the ECG quality of the post-shock ECGrhythm may occur by giving heart rates in the range of 50-90 BPM ahigher score than those above or below that range (with the scoredecreasing the further from that range the heart rates were). Morecomplex scoring systems could additionally or alternatively be used,such as using a windowing function that weights a heart rate of apatient to generate a normalized score. Such a windowing functions mightbe a Hamming window or a Tukey window with a rectangle width that isflat from 50-90 BPM. In each such situation, the data gathered for eachdefibrillation may be saved so that it can be accessed in preparationfor determining and providing identifications of likely success forlater defibrillations.

At box 326, the process determines a combined indicator of success thatincludes an indication from trans-thoracic impedance and an indicationfrom an ECG reading, such as an AMSA indication, and is modifiedappropriately to reflect data about prior successes or failure indefibrillation. The combined indicator may be determined by inputting atrans-thoracic impedance value, an AMSA value, and a count or otherindicator of prior success or failure, into a function or look-up table,or may be determined without a need to compute both or all values first,such as by taking inputs indicative of all values and computing apredictor of success directly from such indicative values. Alternativelyto using a table to calculate the predictive score, the use of logisticregression may be used with a logistic regression equation, with inputsto the equation with, e.g. ECG rhythm type, ECG rate, transthoracicimpedance, prior shocks, etc. Neural network or fuzzy logic methods orother non-linear decision-making methods may also be used. In certaininstances, a single value, like AMSA may be used to compute thelikelihood of success.

At box 328, a success indication is provided to a defibrillatoroperator. The indication may take a variety of forms. For example, theability of the defibrillator to deliver a shock may be enabled when theindicator of success is higher than a threshold level, so that thesuccess indicator is delivered by the operator being shown that a shockcan or cannot be delivered. Also, the operator may be notified that thedefibrillator can provide a shock, and may be prompted to push aphysical button to cause the shock to be delivered.

In some implementations, the operator may also be provided with moredetail about the success indication. For example, the operator may beshown a percentage number that indicates a likelihood in percent thatthe shock will be successful. Alternatively, or in addition, theoperator may be shown a less granular level of an indication, such as avalue of “excellent,” “good,” and “poor” to indicate to the operatorwhat the likelihood of successful defibrillation is.

At box 330, the trigger mechanism is enabled on the defibrillator, asdiscussed above. In certain instances, such a feature may be enabledwhenever a shockable rhythm is observed for a patient. In othercircumstances, the enabling may occur only when the combined indicationdiscussed above exceeds a threshold value for indicating that a shockwill be successful in defibrillating the patient. For a hybriddefibrillator that is capable of manual and AED modes, the triggermechanism may operate different depending on what mode the defibrillatoris in.

An arrow is shown returning to the top of the process to indicate thatthe process here is in ways continuous and in ways repeated. Inparticular, ECG signals are gathered continuously, as are other types ofdata. And the process repeatedly tries to identify whether a shock canor should be provided, and the order and timing of the steps in thatcycling may be dictated by standards as adjusted by a medical directoror other appropriate individual responsible for the deployeddefibrillator. Thus, for instance, the entire process may be repeated,certain portions may be repeated more frequently than others, andcertain portions may be performed once, while others are repeated.

FIG. 3B is a flow chart of a process for identifying a phase in acardiac event so as to provide guidance to a rescuer. In general, theprocess involves using AMSA or other determinations to identify a lengthof time since a cardiac event has begun and/or a phase in which theevent is currently located, where different phases are delineated by therelative likelihood of certain treatment approaches operatingsuccessfully vis-à-vis other phases.

The process in this example begins at box 340, where a patient ismonitored generally, such as by monitoring the patient's pulse and ECG,among other things. Such monitoring may be the same monitoring as instep 320 in FIG. 3B or may occur concurrently with such monitoring. Themonitoring may constitute constantly receiving ECG data and periodicallycomputing (e.g., every second or every two seconds) AMSA and othervalues from it. At the same time, an ECG representation may be displayedto an operator of a defibrillator or other medical device.

At box 342, the AMSA is determined, and may be calculated in knownmanners from the ECG data. Other SPAs may also be operated on theincoming data from the patient. As discussed above, the AMSA value maydepend on a window function of a certain determined length and type, andhaving certain determined coefficients, where each of these parametersmay be adjusted dynamically over the time of a VF incident.

At box 344, prior AMSA measurements are identified. Such a step mayoccur simply by looking to a known location in memory where a softwareprogram has been programmed to store such information. Thosemeasurements or computations may be loaded to a location at which theycan be manipulated relative to each other, including by combining thoseseparate measurements into a composite, such as an average of themeasurements. In obtaining such measurements, the process may fetch onlyn number of prior measurements with each cycle of the process, so that arolling or sliding average is computed at each step. The number ofvalues to combine in any given cycle can be selected so as to providesufficient responsiveness (fewer readings) while providing a sufficientgeneral view of the status of the patient that is not subject to extremefluctuations (more readings).

At box 346, the current general AMSA level (e.g., from an average ofmultiple prior readings) is determined. Other measures of a similar typemay also or alternatively be generated, if they represent theprogression of the patient through nonrecurring phases of a cardiacevent, such as those discussed above.

At box 348, the phase in the progression of the VF event is determinedfor the patient. Such a determination may include simply estimating,with the AMSA level or other such data, the time since the patiententered cardiac arrest, and/or more generally whether the patient is inelectrical, circulatory, or metabolic phase. For example, the AMSA levelfor the patient may be provided to a look-up table that maps observedAMSA values for a population to event phases or time since the eventstarted, or both.

At box 350, the process provides an indication of care that iscorrelated to the phase of the event. For example, a screen on adefibrillator may show a message indicating that the rescuer shouldprepare to administer a shock (e.g., if the patient is in electricalphase and the AMSA determination shows a high likelihood that the shockwill be successful). Similarly, color may be used to show one or more ofthe parameters, such as a single color bar to show likelihood of shocksuccess, where the likelihood is based on current AMSA, combined AMSAvalues (e.g., an average or trend), or a combination of both.

FIG. 4A shows a plot of sensitivity (%) versus AMSA threshold (mv-Hz)for a first set of subjects having a trans-thoracic impedance (TTI)measured greater than 150 ohms, and a second set of subjects having atrans-thoracic impedance measured less than 150 ohms. The data shows theinfluence of TTI on the prediction accuracy of AMSA for shock success atdifferent threshold values as presented in sensitivity and specificity.

The data was obtained by collecting data from defibrillators used inreal rescue events from multiple emergency medical services in theUnited States through regular field case submission to ZOLL MedicalCorporation, and where individual personal identifying information couldnot be determined from the gathered data. All reporting parties usedZOLL automatic external defibrillators that included current-basedimpedance compensation. The sampling rate for ECG data was 250 Hz, andanalysis was performed on a selection of an episode of 2.05 seconds (512data points) ending at 0.5 seconds before each shock attempt. Shocksuccess was defined as an organized rhythm for a minimum of 30 seconds,starting 60 seconds after the delivered shock, and with a rate of 40beats per minute or greater. A total of 1292 shocks (305 successful)form 580 patients with VF were included in the analysis. AMSA. The TTIwas measure at shocking pads placed on each respective subject.

As shown by the comparative data, a patient's TTI affects thepredictability of AMSA by shifting the threshold upward for a givensensitivity or specificity value. AMSA value was significantly higherwhen the TTI was greater than 150 ohm (11.6±8.9 vs. 9.8±7.1, p=0.002) ascompared with those shocks with TTI less than 150 ohm. The AMSAthreshold value was increased from 8.2 mvHz to 10.3 mvHz whensensitivity was set to 85%. Such information can be used to provide areal-time adjustment mechanism, like those discussed above, that adjustsan AMSA threshold for predicting likelihood of shock success orotherwise taking into account the real-time measured TTI so as to affectthe reported likelihood in a manner that makes it more accurate.

FIG. 4B shows a plot of specificity (%) versus AMSA threshold (mv-Hz)for a first set of subjects having a trans-thoracic impedance measuredless than 150 ohms, a second set of subjects having a trans-thoracicimpedance greater than 150 ohms. The tested subjects and data collectionwere the same as for the graph in FIG. 4A. As shown by the comparativedata, AMSA threshold generally increases, for a given specificity, withincreasing trans-thoracic impedance. For example specificity at athreshold of 85% was 11.8 mvHz for TTI<150 ohms, and 14.2 mvHz forTTI>150 ohms. Again, analysis of such data may be used in programmingdevices to provide predictions of likelihood of shock success, or todisable or enable the ability to shock a particular patient, based oncalculated AMSA values.

FIG. 5 shows a defibrillator showing certain types of information thatcan be displayed to a rescuer. In the figure, a defibrillation device500 with a display portion 502 provides information about patient statusand CPR administration quality during the use of the defibrillatordevice. As shown on display 502, during the administration of chestcompressions, the device 500 displays information about the chestcompressions in box on the same display as is displayed a filtered ECGwaveform and a CO2 waveform (alternatively, an SpO2 waveform can bedisplayed).

During chest compressions, the ECG waveform is generated by gatheringECG data points and accelerometer readings, and filtering themotion-induced (e.g., CPR-induced) noise out of the ECG waveform.Measurement of velocity or acceleration of chest compression duringchest compressions can be performed according to the techniques taughtby U.S. Pat. No. 7,220,335, titled Method and Apparatus for Enhancementof Chest Compressions During Chest Compressions, the contents of whichare hereby incorporated by reference in its entirety.

Displaying the filtered ECG waveform helps a rescuer reduceinterruptions in CPR because the displayed waveform is easier for therescuer to decipher. If the ECG waveform is not filtered, artifacts frommanual chest compressions can make it difficult to discern the presenceof an organized heart rhythm unless compressions are halted. Filteringout these artifacts can allow rescuers to view the underlying rhythmwithout stopping chest compressions.

The CPR information in box 514 is automatically displayed whencompressions are detected by a defibrillator. The information about thechest compressions that is displayed in box 514 includes rate 518 (e.g.,number of compressions per minute) and depth 516 (e.g., depth ofcompressions in inches or millimeters). The rate and depth ofcompressions can be determined by analyzing accelerometer readings.Displaying the actual rate and depth data (in addition to, or insteadof, an indication of whether the values are within or outside of anacceptable range) can also provide useful feedback to the rescuer. Forexample, if an acceptable range for chest compression depth is 1.5 to 2inches, providing the rescuer with an indication that his/hercompressions are only 0.5 inches can allow the rescuer to determine howto correctly modify his/her administration of the chest compressions(e.g., he or she can know how much to increase effort, and not merelythat effort should be increased some unknown amount).

The information about the chest compressions that is displayed in box514 also includes a perfusion performance indicator (PPI) 520. The PPI520 is a shape (e.g., a diamond) with the amount of fill that is in theshape differing over time to provide feedback about both the rate anddepth of the compressions. When CPR is being performed adequately, forexample, at a rate of about 101 compressions per minute (CPM) with thedepth of each compression greater than 1.5 inches, the entire indicatorwill be filled. As the rate and/or depth decreases below acceptablelimits, the amount of fill lessens. The PPI 520 provides a visualindication of the quality of the CPR such that the rescuer can aim tokeep the PPI 520 completely filled.

As shown in display 502, the filtered ECG waveform 510 is a full-lengthwaveform that fills the entire span of the display device, while thesecond waveform (e.g., the CO2 waveform 512) is a partial-lengthwaveform and fills only a portion of the display. A portion of thedisplay beside the second waveform provides the CPR information in box514. For example, the display splits the horizontal area for the secondwaveform in half, displaying waveform 512 on left, and CPR informationon the right in box 514.

The data displayed to the rescuer can change based on the actions of therescuer. For example, the data displayed can change based on whether therescuer is currently administering CPR chest compressions to thepatient. Additionally, the ECG data displayed to the user can changebased on the detection of CPR chest compressions. For example, anadaptive filter can automatically turn ON or OFF based on detection ofwhether CPR is currently being performed. When the filter is on (duringchest compressions), the filtered ECG data is displayed and when thefilter is off (during periods when chest compressions are not beingadministered), unfiltered ECG data is displayed. An indication ofwhether the filtered or unfiltered ECG data is displayed can be includedwith the waveform.

Also shown on the display is a reminder 521 regarding “release” inperforming chest compression. Specifically, a fatigued rescuer may beginleaning forward on the chest of a victim and not release pressure on thesternum of the victim at the top of each compression. This can reducethe perfusion and circulation accomplished by the chest compressions.The reminder 521 can be displayed when the system recognizes thatrelease is not being achieved (e.g., signals from an accelerometer showan “end” to the compression cycle that is flat and thus indicates thatthe rescuer is staying on the sternum to an unnecessary degree). Such areminder can be coordinated with other feedback as well, and can bepresented in an appropriate manner to get the rescuer's attention. Thevisual indication may be accompanied by additional visual feedback nearthe rescuer's hands, and by a spoken or tonal audible feedback,including a sound that differs sufficiently from other audible feedbackso that the rescuer will understand that release (or more specifically,lack of release) is the target of the feedback.

Moreover, there is shown in box 522 an indication of a likelihood that ashock, if currently administered, will be effective in defibrillatingthe patient. Here, the likelihood is indicated as being 75%, which isabove a threshold value, so the defibrillator 500 is recommending thatthe rescuer press a button that will operate a switch to cause energy tobe discharged into the patient. The likelihood determination may havebeen made by a process that takes in vector ECG values, and produces anAMSA value (repeatedly) for the patients using such data as it arriveson a plurality of leads that are connected to the patient via electrodesand to the defibrillator 500 wire one or more ports into which thephysical ECG leads can be plugged in a familiar manner.

The particular displays shown in FIG. 5 may be implemented, as notedabove, with a system that uses particular techniques to improve theaccuracy of a prediction that an applied shock will be a success andthat uses AMSA or other SPA values in making such a prediction. Forinstance, the feedback provided by the displays in the figures can bedetermined by selecting an appropriate ECG window size for calculatingAMSA on vectorized values (e.g., one second or slightly longer, such as1.5 seconds or 2 seconds), a window type (e.g., Tukey), and particularcoefficients for the window. Such factors can also be changed over thetime of a VF event, as discussed above, so as to maintain a mostaccurate predictor of defibrillation success.

While at least some of the embodiments described above describetechniques and displays used during manual human-delivered chestcompressions, similar techniques and displays can be used with automatedchest compression devices such as the AUTOPULSE device manufactured byZOLL Medical Corporation of Chelmsford, Mass.

The particular techniques described here may be assisted by the use of acomputer-implemented medical device, such as a defibrillator thatincludes computing capability. The computing portions of suchdefibrillator or other device is shown generally in FIG. 6, and maycommunicate with and/or incorporate a computer system 600 in performingthe operations discussed above, including operations for computing thequality of one or more components of CPR provided to a victim andgenerating feedback to rescuers, including feedback to change rescuerswho are performing certain components of the CPR. The system 600 may beimplemented in various forms of digital computers, includingcomputerized defibrillators laptops, personal digital assistants,tablets, and other appropriate computers. Additionally the system caninclude portable storage media, such as, Universal Serial Bus (USB)flash drives. For example, the USB flash drives may store operatingsystems and other applications. The USB flash drives can includeinput/output components, such as a wireless transmitter or USB connectorthat may be inserted into a USB port of another computing device.

The system 600 includes a processor 610, a memory 620, a storage device630, and an input/output device 640. Each of the components 610, 620,630, and 640 are interconnected using a system bus 650. The processor610 is capable of processing instructions for execution within thesystem 600. The processor may be designed using any of a number ofarchitectures. For example, the processor 610 may be a CISC (ComplexInstruction Set Computers) processor, a RISC (Reduced Instruction SetComputer) processor, or a MISC (Minimal Instruction Set Computer)processor.

In one implementation, the processor 610 is a single-threaded processor.In another implementation, the processor 610 is a multi-threadedprocessor. The processor 610 is capable of processing instructionsstored in the memory 620 or on the storage device 630 to displaygraphical information for a user interface on the input/output device640.

The memory 620 stores information within the system 600. In oneimplementation, the memory 620 is a computer-readable medium. In oneimplementation, the memory 620 is a volatile memory unit. In anotherimplementation, the memory 620 is a non-volatile memory unit.

The storage device 630 is capable of providing mass storage for thesystem 600. In one implementation, the storage device 630 is acomputer-readable medium. In various different implementations, thestorage device 630 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device.

The input/output device 640 provides input/output operations for thesystem 600. In one implementation, the input/output device 640 includesa keyboard and/or pointing device. In another implementation, theinput/output device 640 includes a display unit for displaying graphicaluser interfaces.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device for execution by a programmableprocessor; and method steps can be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput. The described features can be implemented advantageously in oneor more computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having an LCD (liquid crystal display) or LED display fordisplaying information to the user and a keyboard and a pointing devicesuch as a mouse or a trackball by which the user can provide input tothe computer.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include a local area network (“LAN”),a wide area network (“WAN”), peer-to-peer networks (having ad-hoc orstatic members), grid computing infrastructures, and the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Many other implementations other than those described may be employed,and may be encompassed by the following claims.

What is claimed is:
 1. A system for managing care of a person, thesystem comprising: one or more capacitors arranged to deliver adefibrillating shock to a person; one or more electronic ports forreceiving a plurality of signals from sensors for obtaining indicationsof an electrocardiogram (ECG) for the person; and a patient treatmentmodule executable on one or more computer processors using code storedin non-transitory media and arranged to provide a determination of alikelihood of success from delivering a future defibrillating shock tothe person with the one or more capacitors, using a mathematicalcomputation applied to a vector value defined by signals from at leasttwo of the plurality of signals.
 2. The system of claim 1, wherein themathematical computation comprises one or more vectorized Fast FourierTransforms (FFTs).
 3. The system of claim 2, wherein the mathematicalcomputation comprises one or more amplitude spectrum area calculations.4. The system of claim 1, wherein the patient treatment modules isfurther arranged to apply a pre-transform to the plurality of signalsbefore applying the mathematical computation so as to make the pluralityof signals orthogonal or near orthogonal to each other.
 5. The system ofclaim 4, wherein the pre-transform is applied in response to determiningthat the plurality of signals were not previously orthogonal or nearorthogonal.
 6. The system of claim 4, wherein the patient treatmentmodule is programmed to apply the mathematical computation to the vectorvalue by calculating FFT for each of the plurality of signals to createprocessed values and then combining the processed values.
 7. The systemof claim 6, wherein combining the processed values comprises determininga root of a sum of the processed values.
 8. The system of claim 1,wherein the mathematical computation comprises a mathematical transformfrom a time domain to a frequency domain on a window of data.
 9. Thesystem of claim 8, wherein the window comprises a tapered window. 10.The system of claim 9, wherein the tapered window comprises a Tukeywindow.
 11. The system of claim 10, wherein the tapered window isbetween about one second and about 2 seconds in width.
 12. The system ofclaim 9, wherein the tapered window is selected from a group consistingof Tukey, Hann, Blackman-Harris, and Flat Top.
 13. The system of claim1, further comprising an output mechanism arranged to present, to a userof the system, an indication regarding the likelihood of success fromdelivering a defibrillating shock with the one or more capacitors to theperson.
 14. The system of claim 13, wherein the output mechanismcomprises a visual display, and the system is programmed to display tothe user one of multiple possible indications that each indicate adegree of likelihood of success.
 15. The system of claim 13, wherein theoutput mechanism comprises an interlock that prevents a user fromdelivering a shock unless the determined likelihood of success exceeds adetermined value.
 16. The system of claim 1, wherein the patienttreatment module comprises an ECG analyzer for generating an amplitudespectrum area (AMSA) value using a transform.
 17. The system of claim16, where the patient treatment module is programmed to determinewhether a prior defibrillation shock was at least partially successful,and based at least in part on the determination of whether the priordefibrillation was at least partially successful, modifying acalculation of the likelihood of success from delivering the futuredefibrillating shock.
 18. The system of claim 1, wherein determining alikelihood of success from delivering a future defibrillating shock tothe person depends on a determination of whether one or more priorshocks delivered to the person were successful in defibrillating theperson.
 19. The system of claim 1, wherein the mathematical computationcomprises a transform selected from a group consisting of Fourier,discrete Fourier, Hilbert, discrete Hilbert, wavelet, and discretewavelet methods.
 20. The system of claim 1, wherein the patienttreatment module is programmed to determine the likelihood of successfrom delivering a future defibrillating shock using at least onepatient-dependent physical parameter separate from a patient ECGreading.
 21. The system of claim 20, wherein the patient treatmentmodule is programmed to determine the likelihood of success fromdelivering a future defibrillating shock using a measure oftrans-thoracic impedance of the person.
 22. A method for managing careof a person, the method comprising: monitoring, with an externaldefibrillator, electrocardiogram (ECG) data from a person receivingemergency cardiac assistance, the ECG data defining a vector frommultiple ECG signals; performing a vectorized mathematical transform ofthe ECG data that defines the vector from a time domain to a frequencydomain using a window in the time domain; determining a likelihood offuture defibrillation shock success using at least the mathematicaltransformation; and affecting control of the external defibrillatorbased on an identification of whether a present defibrillation shockwill likely be effective.
 23. The method of claim 22, wherein themathematical transform comprises one or more vectorized Fast FourierTransforms (FFTs).
 24. The method of claim 23, wherein the mathematicaltransform comprises one or more amplitude spectrum area (AMSA)calculations.
 25. The method of claim 22, further comprising applying apre-transform to a plurality of signals before applying the mathematicaltransform so as to make the plurality of signals orthogonal or nearorthogonal.
 26. The method of claim 25, wherein the pre-transform isapplied in response to determining that the plurality of signals werenot previously orthogonal or near orthogonal.
 27. The method of claim26, further comprising applying the mathematical transform to the vectorvalue by calculating FFT for each of the plurality of signals to createprocessed values, and then combining the processed values.
 28. Themethod of claim 27, wherein combining the processed values comprisesdetermining a root of a sum of the processed values.
 29. The method ofclaim 22, wherein the mathematical transform comprises a transform froma time domain to a frequency domain on a window of data.
 30. The methodof claim 22, wherein the mathematical transform comprises a transformfrom a time domain to a frequency domain on a window of ECG data. 31.The method of claim 30, wherein the window comprises a tapered window.32. The method of claim 31, wherein the tapered window comprises a Tukeywindow.
 33. The method of claim 31, wherein the tapered window isbetween about one second and about two seconds wide.
 34. The method ofclaim 31, wherein the tapered window is selected from a group consistingof Tukey, Hann, Blackman-Harris, and Flat Top.
 35. The method of claim30, wherein the mathematical transform comprises a Fast FourierTransform.
 36. The method of claim 22, wherein determining a likelihoodof future defibrillation shock success comprises determining a valuethat is a function of electrocardiogram amplitude at particulardifferent frequencies or frequency ranges.
 37. The method of claim 36,wherein determining a likelihood of future defibrillation shock successcomprises determining an amplitude spectrum area (AMSA) value for theECG data.
 38. The method of claim 37, wherein determining a likelihoodof future defibrillation shock success further comprises adjusting thedetermined AMSA value using information about a prior defibrillationshock.
 39. The method of claim 38, further comprising determiningwhether the adjusted AMSA value exceeds a predetermined threshold value.40. The method of claim 39, further comprising providing to a rescuer avisual, audible, or tactile alert that a shockable situation exists forthe person receiving emergency cardiac assistance, if the adjusted AMSAvalue is determined to exceed the predetermined threshold value.
 41. Themethod of claim 22, further comprising determining whether a priordefibrillation shock was at least partially successful, and based atleast in part on the determination of whether the prior defibrillationwas at least partially successful, modifying a calculation of thelikelihood of success from delivering the future defibrillating shock.42. The method of claim 22, wherein determining a likelihood of successfrom delivering a future defibrillating shock comprises performing acalculation by an operation selected form a group consisting of logisticregression, table look-up, neural network, and fuzzy logic.
 43. Themethod of claim 22, where the likelihood of success from delivering afuture defibrillating shock is determined using at least onepatient-dependent physical parameter separate from a patient ECGreading.
 44. The method of claim 43, wherein the at least onepatient-dependent parameter comprises an indication of trans-thoracicimpedance of the person receiving emergency cardiac care.
 45. The methodof claim 44, wherein the indication of trans-thoracic impedance isdetermined from signals sensed by a plurality of electrocardiogram leadsthat also provide the ECG data.
 46. The method of claim 22, furthercomprising cyclically repeating the actions of monitoring, determining,identifying, and affecting the control.
 47. The method of claim 22,further comprising identifying compression depth of chest compressionsperformed on the person receiving emergency cardiac assistance, using adevice on the person's sternum and in communication with the externaldefibrillator, and providing feedback to a rescuer performing the chestcompressions, the feedback regarding rate of compression, depth ofcompression, or both.
 48. The method of claim 22, wherein affectingcontrol of the defibrillator comprises preventing a user from deliveringa shock unless the determination of whether a shock will be effectiveexceeds a determined likelihood level.
 49. The method of claim 22,wherein affecting control of the defibrillator comprises electronicallydisplaying, to a user, an indicator of the determined indication ofwhether a shock will be effective.
 50. The method of claim 49, whereindisplaying an indicator comprises displaying a value, of multiplepossible values in a range, that indicates a likelihood of success. 51.The method of claim 50, wherein the calculation of the likelihood ofcurrent shock success is determined or modified using a determination ofa value of trans-thoracic impedance of the person.